ONNX Runtime is a high-performance inference engine for deploying machine learning models. It supports models in the ONNX (Open Neural Network Exchange) format, which is an open standard for representing machine learning models. The primary purpose of ONNX Runtime is to provide a fast and efficient way to run models across different platforms and devices.
When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 36 : FAIL : Model validation failed. This error indicates that the model you are trying to load or execute has failed the validation checks required by ONNX Runtime.
Developers often see this error when attempting to load a model that has been improperly converted to the ONNX format or when there are compatibility issues between the model and the ONNX Runtime version being used.
The error code 36 in ONNX Runtime signifies a failure in model validation. This typically occurs when the model does not adhere to the ONNX specification or contains unsupported operations or attributes. Validation is crucial to ensure that the model can be executed correctly and efficiently by the runtime.
To resolve the model validation failure, follow these steps:
Use the ONNX Model Checker to validate your model. This tool checks the model against the ONNX specification and reports any issues.
python -m onnx.checker model.onnx
Address any errors reported by the checker.
Ensure that all operators used in your model are supported by the version of ONNX Runtime you are using. You can find the list of supported operators in the ONNX Runtime documentation.
If the model was converted from another framework, ensure that the conversion process was done correctly. Use tools like tf2onnx or PyTorch ONNX exporter and follow their guidelines for a successful conversion.
Ensure that you are using the latest version of ONNX Runtime, as newer versions may have better support for certain operators and features. Update ONNX Runtime using pip:
pip install --upgrade onnxruntime
By following these steps, you should be able to resolve the model validation failure in ONNX Runtime. Ensuring that your model adheres to the ONNX specification and is compatible with the runtime version is crucial for successful deployment. For more detailed guidance, refer to the ONNX Runtime documentation.
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